Getting the Most From Facebook Topic Data: Part One

Disruptive is an extremely overused word in technology, but there is no avoiding it here – Facebook is one of the most disruptive businesses ever to launch. Pause for a moment to consider just how impactful Facebook has been: it has changed the way we communicate; how we consume news; how we share photos; how we interact with brands; almost every facet of our lives has been altered by Facebook.

It’s no surprise then to learn that of Facebook’s 1.65 billion monthly active users, each spends an average of 50 minutes a day on the platform. The data points that its users generate, mean that Facebook has also caused a major disruption within marketing.

We believe Facebook topic data is one of the greatest leaps forward in the history of marketing and, by using PYLON, companies can understand, engage with and reach their audience like never before. To help marketers do this, we will be publishing a series of blogs over the next few weeks that explore how to get the most from Facebook topic data. We hope you enjoy these and find them of use.

Different kinds of social network

Before we get into the detail of PYLON and Facebook topic data, let’s look at the types of social platform to have emerged over the past decade:

Public networks – many people now contact brands directly when they have a product or service issue. Either with an @mention on Twitter or a post on the brand’s Facebook page.

Walled gardens – discussions about what to buy largely occur on non-public networks, away from the prying eyes of brands. Someone seeking advice on a purchasing decision can do so within the relative safety of a Facebook friend network, or professional network on LinkedIn.

One-to-one networks – one-to-one and small group messaging networks are the fastest growing communication services in 2016. Platforms such as Snapchat, Facebook Messenger and WhatsApp offer something previously missing from the social network landscape.

Image-oriented networks – visual networks like Instagram present a unique challenge for social data analysts, because text is so sparse.

Although all four types have their own purpose for different audiences, there is a definite trend towards to non-public networks such as walled gardens and one-to-one messaging networks. Consumers are rightly concerned about their data privacy and are less willing to share opinions in a public forum. As a result, the past few years have seen people shift towards non-public networks.

The enormous value of non-public social data

There are significantly more people on non-public networks – particularly Facebook – which means the data is far more representative of the wider population and less skewed toward a particular demographic group. It also means that actual usage is more evenly spread across all demographic groups.

The quality of data is also much stronger. On public networks, people are often more self-promotional and can have different agendas. People on non-public social networks are posting for their friends and family about their real lives and are more likely to share personal preferences, interests and intent.

In short, the sheer number of users and volume of content that people share and engage around on Facebook, means that it is one of the world’s largest sources of public opinion. However, access to non-public data sources for brands and marketers has been difficult to gain – until now.

Facebook topic data

The social data ecosystem has had to recognise that the approach to analysing data is different for the three types of non-public network. For walled garden networks such as Facebook, most text or personal identifiable information cannot be legitimately extracted.

However, social analytics providers are now realising that stepping away from ‘individual-level data’ and working with ‘anonymised aggregated data’ enables social networks to share more information than was previously available. For marketers, this akin to the Holy Grail.

Facebook topic data now allows insights to be drawn from posts, likes, comments and shares across the entire Facebook platform. It includes detailed demographic information, as well as link data and eponymous topics. All engagements are anonymised before they are made available for analysis and the output of any analysis is always aggregated data, delivering true insight.

Analytics teams have had to change their mindset and skillset to look across such large and rich datasets of audience demographics and interests. A new era is dawning in the social data analytics industry built on deeper insights from data that has never been available before. It is an exciting time to be studying consumer relationships with brands, products and experiences in a privacy-first, multi-dimensional way.

This is where DataSift’s PYLON platform comes in. It allows analysis of what people are sharing and posting on non-public networks in an aggregated and anonymised format, protecting the privacy of the people on it. We’ll look at PYLON in more detail in the next blog in this series.